Research trends in deep learning and machine learning for cloud computing security Article Swipe
Yehia Ibrahim Alzoubi
,
Alok Mishra
,
Ahmet E. Topcu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10462-024-10776-5
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10462-024-10776-5
Deep learning and machine learning show effectiveness in identifying and addressing cloud security threats. Despite the large number of articles published in this field, there remains a dearth of comprehensive reviews that synthesize the techniques, trends, and challenges of using deep learning and machine learning for cloud computing security. Accordingly, this paper aims to provide the most updated statistics on the development and research in cloud computing security utilizing deep learning and machine learning. Up to the middle of December 2023, 4051 publications were identified after we searched the Scopus database. This paper highlights key trend solutions for cloud computing security utilizing machine learning and deep learning, such as anomaly detection, security automation, and emerging technology's role. However, challenges such as data privacy, scalability, and explainability, among others, are also identified as challenges of using machine learning and deep learning for cloud security. The findings of this paper reveal that deep learning and machine learning for cloud computing security are emerging research areas. Future research directions may include addressing these challenges when utilizing machine learning and deep learning for cloud security. Additionally, exploring the development of algorithms and techniques that comply with relevant laws and regulations is essential for effective implementation in this domain.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1007/s10462-024-10776-5
- OA Status
- hybrid
- Cited By
- 32
- References
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All OpenAlex metadata
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https://openalex.org/W4396578638Canonical identifier for this work in OpenAlex
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https://doi.org/10.1007/s10462-024-10776-5Digital Object Identifier
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Research trends in deep learning and machine learning for cloud computing securityWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2024Year of publication
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2024-05-02Full publication date if available
- Authors
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Yehia Ibrahim Alzoubi, Alok Mishra, Ahmet E. TopcuList of authors in order
- Landing page
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https://doi.org/10.1007/s10462-024-10776-5Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1007/s10462-024-10776-5Direct OA link when available
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Computer science, Cloud computing, Deep learning, Artificial intelligence, Cloud computing security, Machine learning, Computer security, Operating systemTop concepts (fields/topics) attached by OpenAlex
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32Total citation count in OpenAlex
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2025: 25, 2024: 7Per-year citation counts (last 5 years)
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120Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.searched | 88 |
| abstract_inverted_index.security | 13, 68, 101, 112, 159 |
| abstract_inverted_index.threats. | 14 |
| abstract_inverted_index.computing | 48, 67, 100, 158 |
| abstract_inverted_index.database. | 91 |
| abstract_inverted_index.effective | 200 |
| abstract_inverted_index.essential | 198 |
| abstract_inverted_index.exploring | 183 |
| abstract_inverted_index.learning, | 107 |
| abstract_inverted_index.learning. | 74 |
| abstract_inverted_index.published | 21 |
| abstract_inverted_index.security. | 49, 143, 181 |
| abstract_inverted_index.solutions | 97 |
| abstract_inverted_index.utilizing | 69, 102, 173 |
| abstract_inverted_index.addressing | 11, 169 |
| abstract_inverted_index.algorithms | 187 |
| abstract_inverted_index.challenges | 38, 119, 133, 171 |
| abstract_inverted_index.detection, | 111 |
| abstract_inverted_index.directions | 166 |
| abstract_inverted_index.highlights | 94 |
| abstract_inverted_index.identified | 85, 131 |
| abstract_inverted_index.statistics | 59 |
| abstract_inverted_index.synthesize | 33 |
| abstract_inverted_index.techniques | 189 |
| abstract_inverted_index.automation, | 113 |
| abstract_inverted_index.development | 62, 185 |
| abstract_inverted_index.identifying | 9 |
| abstract_inverted_index.regulations | 196 |
| abstract_inverted_index.techniques, | 35 |
| abstract_inverted_index.Accordingly, | 50 |
| abstract_inverted_index.publications | 83 |
| abstract_inverted_index.scalability, | 124 |
| abstract_inverted_index.technology's | 116 |
| abstract_inverted_index.Additionally, | 182 |
| abstract_inverted_index.comprehensive | 30 |
| abstract_inverted_index.effectiveness | 7 |
| abstract_inverted_index.implementation | 201 |
| abstract_inverted_index.explainability, | 126 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.99327205 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |